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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 5139574, 11 pages
http://dx.doi.org/10.1155/2016/5139574
Research Article

On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study

Dipartimento di Ingegneria dell’Innovazione, Università del Salento, 73100 Lecce, Italy

Received 17 March 2016; Accepted 30 October 2016

Academic Editor: Jens Christian Claussen

Copyright © 2016 Massimo Pacella et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In modern industry, the development of complex products involves engineering changes that frequently require redesigning or altering the products or their components. In an engineering change process, engineering change requests (ECRs) are documents (forms) with parts written in natural language describing a suggested enhancement or a problem with a product or a component. ECRs initiate the change process and promote discussions within an organization to help to determine the impact of a change and the best possible solution. Although ECRs can contain important details, that is, recurring problems or examples of good practice repeated across a number of projects, they are often stored but not consulted, missing important opportunities to learn from previous projects. This paper explores the use of Self-Organizing Map (SOM) to the problem of unsupervised clustering of ECR texts. A case study is presented in which ECRs collected during the engineering change process of a railways industry are analyzed. The results show that SOM text clustering has a good potential to improve overall knowledge reuse and exploitation.